Data Engineer

Samsung Food pioneers the future of everyday care and health by connecting food, health, and home across the Samsung ecosystem. The company develops personalized guidance and intelligent data products, including Vision AI and integrations with Samsung Health and SmartThings, to improve daily life for millions of users globally.

• Build and evolve the data platform by designing, developing, and maintaining scalable batch and streaming pipelines;
• Model data for scale and usability using dimensional modeling and Kimball principles;
• Own transformation workflows in dbt, improving testing, documentation, lineage, and reliability;
• Optimize data storage and query performance in ClickHouse;
• Improve platform reliability, orchestration, observability, data quality, and failure recovery in Kubernetes;
• Drive engineering standards for development, testing, deployment, versioning, and monitoring;
• Champion data governance principles including quality, ownership, and consistency;
• Partner with product managers, analysts, and engineers to translate business needs into technical solutions;
• Lead architecture decisions, mentor engineers, and influence data engineering practices.

• 4+ Years of experience in data engineering building production-grade data platforms;
• Deep expertise in Python and SQL;
• Proven experience with workflow orchestration tools like Airflow;
• Strong experience designing and optimizing analytical data stores, ideally ClickHouse;
• Practical experience with dbt and modern ELT patterns;
• Experience with event-driven or streaming architectures using Kafka;
• Experience deploying and operating data workloads in Kubernetes;
• Strong knowledge of dimensional modeling and Kimball methodology;
• Proactive, ownership-driven mindset with strong communication skills;
• Experience using AI-assisted development tools to improve engineering efficiency;
• Interest in or experience with designing, integrating, or deploying AI agents and internal automation tools;
• Nice to have: Experience with cloud environments (GCP), building platforms for analytics and operational use cases, data quality frameworks, observability tooling, CI/CD practices, consumer products, health, food-tech, or personalization-heavy environments, mentoring engineers, and data governance principles.

• No conditions specified